Flow rate control simulator for optimizing indoor air and control method therefor

Abstract

The present invention relates to a flow rate control simulator for optimizing indoor air, in which experimental data usable for developing an optimal control algorithm of a variable air volume (VAV) system can be secured in a short period of time, and a control method therefor. The flow rate control simulator includes: an experimental space modeling unit that sets a control region and assumption for VAV system application; a governing equation selection and analysis unit for selecting a governing equation for each control variable; a governing equation linearization unit that performs discretization through linearization of the governing equation for calculation; a governing equation solution processing unit that applies a solution for solving the governing equation; a simulator interface unit for providing a simulator interface by applying a state-space model formula; and a result confirmation and verification unit for confirming and verifying a result through simulation execution.

Description

Flow control simulator for indoor air optimization and its control method

[0001] The present invention relates to indoor air optimization, and more particularly, to a flow control simulator for indoor air optimization and a control method thereof, which enable obtaining experimental data usable for developing an optimal control algorithm for a VAV (Variable Air Volume) system in a short period of time.

[0002] Due to the increase in tall buildings, research and development is continuously being conducted to reduce the energy costs of air conditioning systems.

[0003] Here, the air conditioning system refers to a facility technology that controls the temperature by introducing hot or cold air into various indoor spaces through ceiling pipes or removes dust through ventilation.

[0004] The VAV (Variable Air Volume) system, a representative air conditioning system, distributes the cooling and heating flow rate to each zone (room) according to the required cooling and heating load in a centrally controlled system such as a large building. However, due to the continuous increase in high-rise buildings, the need for technology development for 'energy saving' to efficiently distribute the given resources is emerging.

[0005] Figure 1 is a configuration diagram of a typical VAV (Variable Air Volume) system.

[0006] Energy consumption in air conditioning facilities accounts for a large portion of management costs, and research is being conducted to create a comfortable environment and save energy.

[0007] The VAV system is the most common air conditioning system, and quantitative comparative research is actively being conducted in terms of total energy consumption through minimum air volume control for the VAV system.

[0008] Currently, most domestic automatic control systems depend on imports, and research and development is not active compared to overseas, and research on multi-stage control (cascade control) is in progress to solve problems such as air volume concentration, which is a problem of VAV.

[0009] In VAV modeling and simulation, it is divided into Steady state and Unsteady state, and most research is on Unsteady state.

[0010] Building modeling is a problem of complexity of the components to be modeled, so full-scale studies of building models are rare, and modeling within a limited scope is predominant.

[0011] Research is ongoing to evaluate the energy efficiency of VAV systems by comparing them with other HVAC systems, and it is being proven that VAV systems have the potential to save 30-50% of energy compared to CAV systems.

[0012] VAV modeling is continuously being studied using mathematical and empirical models, but there are flaws arising from various assumptions and uncertainties in thermal characteristics, and research to improve these is ongoing. In addition, although the VAV system is the most energy-efficient system, there is still room for improvement in energy efficiency, so ongoing research is being conducted.

[0013] Figure 2 is a schematic diagram showing the experimental data collection process for an indoor space in the prior art.

[0014] In the conventional technology, in order to collect experimental data on indoor spaces, an actual space was directly constructed, factors that met the desired conditions were input, the results were confirmed, and the final results were collected in the form of data.

[0015] However, this method requires a lot of time to collect experimental data and is also costly to set up an experimental environment.

[0016] In this way, when using the experimental method of the prior art, there is a problem that it takes not only cost but also several months or more to obtain measurement results for various conditions.

[0017] Therefore, there is a need for the development of a new technology that can generate experimental data usable for developing an optimal control algorithm for a system in a short period of time, thereby enabling the development of an efficient optimal control algorithm.

[0018] The present invention is intended to solve the problems of conventional indoor air optimization technology, and provides a flow control simulator for indoor air optimization and a control method thereof that can secure experimental data usable for developing an optimal control algorithm for a VAV (Variable Air Volume) system in a short period of time.

[0019] The purpose of the present invention is to provide a flow control simulator for indoor air optimization and a control method thereof, which can provide experimental data necessary for developing an optimal control algorithm for a VAV system in a short period of time by defining a differential equation of a control variable and estimating the amount of change in the control variable through computer calculation.

[0020] The purpose of the present invention is to provide a flow control simulator for optimizing indoor air quality and a control method thereof, which enables simulation for developing an optimal control algorithm efficiently in terms of cost and time by estimating the amount of change in a control variable only through matrix and vector equation calculations using a computer.

[0021] The purpose of the present invention is to provide a flow control simulator for indoor air optimization and a control method thereof, which can efficiently collect simulation results according to control variables including external temperature, number of heat sources, and room size by constructing a simulation system through mathematical operations that can obtain measurement results for various conditions in a short period of time.

[0022] Other purposes of the present invention are not limited to the purposes mentioned above, and other purposes not mentioned will be clearly understood by those skilled in the art from the description below.

[0023] In order to achieve the above object, the present invention provides a flow control simulator for indoor air optimization, which comprises an electronic device including at least one processor for flow control simulation for indoor air optimization, comprising: an experimental space modeling unit for setting up a control area and assumptions for applying a VAV system; a governing equation selection and analysis unit for selecting governing equations for each control variable; a governing equation linearization unit for performing discretization through linearization of the governing equations for calculation; a governing equation solution processing unit for applying a solution for solving the governing equations; a simulator interface unit for providing a simulator interface by applying a state-space model formula; and a result confirmation and verification unit for confirming and verifying results through simulation execution.

[0024] A control method of a flow control simulator for optimizing indoor air according to the present invention for achieving another purpose is characterized by including the steps of modeling an experimental space that sets a control area and assumptions for applying a VAV system, selecting a governing equation for each control variable; performing discretization through linearization of the governing equation for calculation, and applying a solution for solving the governing equation; reading an initial value of a simulation to set an initial value of a simulation; defining values ​​used in a variation amount and the governing equation and starting a simulation; setting an initial value of the governing equation, calculating a variation amount by zone, and applying the calculated variation amount by zone.

[0025] The flow control simulator and its control method for optimizing indoor air according to the present invention as described above have the following effects.

[0026] First, it enables us to secure experimental data that can be used to develop an optimal control algorithm for a VAV (Variable Air Volume) system in a short period of time.

[0027] Second, by defining the differential equation of the control variable and estimating the amount of change in the control variable through computer calculation, it is possible to provide experimental data necessary for developing an optimal control algorithm for the VAV system in a short period of time.

[0028] Third, it enables simulation for developing an optimal control algorithm in an efficient manner in terms of cost and time by estimating the amount of change in the control variable only through matrix and vector equation operations using a computer.

[0029] Fourth, a simulation system using mathematical operations that can obtain measurement results for various conditions in a short period of time is established, enabling efficient collection of simulation results according to control variables including external temperature, number of heat sources, and room size.

[0030] Figure 1 is a configuration diagram of a typical VAV (Variable Air Volume) system.

[0031] Figure 2 is a diagram showing the experimental data collection process for an indoor space in the prior art.

[0032] Figure 3 is a schematic diagram showing the operation concept of a flow control simulator for indoor air optimization according to the present invention.

[0033] Figure 4 is a block diagram of a flow control simulator for indoor air optimization according to the present invention.

[0034] Figure 5 is a model diagram of the experimental space work zone.

[0035] Figure 6 is a schematic diagram of material movement in the work zone.

[0036] Figure 7 is a configuration diagram showing an example of a control variable governing equation applied to a flow control simulator for indoor air optimization according to the present invention.

[0037] Figure 8 is a diagram showing the process of solving the governing equation.

[0038] Figure 9 is a configuration diagram showing an example of a simulator GUI.

[0039] Figure 10 is a graph showing simulation results according to one embodiment of the present invention.

[0040] Figure 11 is a flow chart showing a control method of a flow control simulator for indoor air optimization according to the present invention.

[0041] Figures 12a and 12b are graphs showing simulation results of a flow control simulator for indoor air optimization according to the present invention.

[0042] Hereinafter, preferred embodiments of a flow control simulator and a control method thereof for indoor air optimization according to the present invention will be described in detail.

[0043] The features and advantages of the flow control simulator and its control method for indoor air optimization according to the present invention will become apparent through the detailed description of each embodiment below.

[0044] Figure 3 is a schematic diagram showing the operation concept of a flow control simulator for indoor air optimization according to the present invention.

[0045] The terms used in this disclosure have been selected from widely used, current terms, taking into account the functions of the disclosure. However, these terms may vary depending on the intentions of those skilled in the art, precedents, the emergence of new technologies, etc. Furthermore, in certain cases, terms may be arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant description of the invention. Therefore, the terms used in this disclosure should not be defined simply as names, but rather based on the meanings of the terms and the overall content of the disclosure.

[0046] When a part of the specification is said to "include" a component, this does not exclude other components, but rather implies the inclusion of other components, unless otherwise specifically stated. Furthermore, terms such as "part," "module," etc., used throughout the specification refer to a unit that processes at least one function or operation, which may be implemented in hardware, software, or a combination of hardware and software.

[0047] In particular, units that process at least one function or operation may be implemented as an electronic device including at least one processor, and at least one peripheral device may be connected to the electronic device depending on the method of processing the function or operation. The peripheral devices may include a data input device, a data output device, and a data storage device.

[0048] The flow control simulator for indoor air optimization according to the present invention and its control method enable acquisition of experimental data usable for developing an optimal control algorithm for a VAV (Variable Air Volume) system in a short period of time.

[0049] To this end, the present invention may include a configuration that defines a differential equation of a control variable and estimates the amount of change in the control variable through computer calculation, thereby providing experimental data necessary for developing an optimal control algorithm for a VAV system in a short period of time.

[0050] The present invention may include a configuration for estimating the amount of change in a control variable using only matrix and vector equation operations using a computer to enable simulation for developing an optimal control algorithm efficiently in terms of cost and time.

[0051] The change is calculated by the corresponding governing equations for the variables to be controlled, temperature, humidity, and carbon dioxide. For the temperature variable, it is derived by the Energy Equation, and for the humidity / carbon dioxide, it is derived by the Mass Balance equation.

[0052] As in Fig. 3, the input values ​​of the simulator are written in a text file and used, and values ​​that do not change during the simulation operation, such as the volume of the zone, are set only once at the beginning of the simulation.

[0053] And when the simulation starts by defining the values ​​used in the change amount and governing equation, the process of setting the initial value and calculating and applying the state change amount is repeated until the simulation time reaches the end time.

[0054] Figure 4 is a block diagram of a flow control simulator for indoor air optimization according to the present invention.

[0055] The flow control simulator for optimizing indoor air according to the present invention includes an experimental space modeling unit (10) that sets a control area and assumptions for applying a VAV system, a governing equation selection and analysis unit (20) that selects governing equations for each control variable, a governing equation linearization unit (30) that performs discretization through linearization of the governing equations for calculation, a governing equation solution processing unit (40) that applies a solution for solving the governing equations, a simulator interface unit (50) that provides a simulator interface by applying a state-space model formula, and a result confirmation and verification unit (60) that confirms and verifies results through simulation execution.

[0056] The experimental space modeling is as follows.

[0057] Figure 5 is a schematic diagram of the experimental space Work Zone, and Figure 6 is a schematic diagram of material movement in the Work Zone.

[0058] In one embodiment of the present invention, the Work Zone to be used for simulator development may use a three-dimensional space in the form of 3.48 x 2.5 x 2.7 (unit: m) as shown in FIG. 5.

[0059] For efficient operation of the simulator, assumptions about the work zone can be made as in Table 1, and a schematic diagram for material movement is as in Fig. 6.

[0060]

[0061] The Work Zone is assumed to be a lumped system, and the walls connected to the outside are assumed to have no heat capacity, so heat exchange between the Work Zone and the outside occurs only through conduction, and the Zones are completely blocked, and the incoming and outgoing masses are assumed to be equal.

[0062] Figure 7 is a schematic diagram showing an example of a control variable governing equation applied to a flow control simulator for indoor air optimization according to the present invention.

[0063] In one embodiment of the present invention, the variables to be controlled are three: temperature, humidity, and carbon dioxide, and the change amount of each control variable is calculated using the corresponding governing equation.

[0064] For temperature variables, they are derived from the Energy Equation, and for humidity and carbon dioxide, they are derived from the Mass Balance equation.

[0065] The temperature differential equation states that the heat change rate inside the work zone is equal to the heat change rate of the supply air, the heat load on the wall, and the heat load generated by people.

[0066]

[0067] Here, the variable ρ is density, V is volume, C is specific heat capacity, G is air flow rate, T is temperature, A is area, U is heat transfer coefficient, Q is heat amount, and N is the number of heat sources.

[0068] And the subscript a is air, a, n are the values ​​escaping to the outside, a, s are the values ​​coming into the room, wall is the wall, out is the outside, and i is the wall number.

[0069] The humidity differential equation states that the rate of change in humidity inside the work zone is equal to the sum of the rate of change in humidity due to the supplied air and the rate of change in humidity due to human breathing.

[0070]

[0071] Here, the variable ρ is density, V is volume, G is air flow rate, W is humidity, A is area, and N is the number of heat sources.

[0072] And the subscript a is air, a, n are the values ​​escaping outside, a, s are the values ​​coming into the room, wall is the wall, out is outside, i is the wall number, exhale is the exhalation.

[0073] And the carbon dioxide differential equation is that the carbon dioxide change rate inside the work zone is equal to the carbon dioxide change rate due to the supplied air and the carbon dioxide change rate due to human breathing.

[0074]

[0075] Here, the variable ρ is density, V is volume, G is air flow rate, C is carbon dioxide, and N is the number of heat sources.

[0076] And the subscript a is air, a, n are the values ​​escaping outside, a, s are the values ​​coming into the room, wall is the wall, out is outside, i is the wall number, exhale is the exhalation.

[0077] Figure 8 is a diagram showing the process of solving the governing equation.

[0078] To perform computer operations on differential equations, a linearization process is required.

[0079] Therefore, in order to reflect the change in the State value according to the input value and to enable computer calculation of the nonlinear system, a linearization process is performed to change the variable values ​​into the form of initial values ​​and change amounts.

[0080]

[0081] Mathematical expression 5 is an expression for linearized temperature change.

[0082]

[0083] Mathematical expression 6 is an expression for linearized humidity change.

[0084]

[0085] Mathematical expression 7 is the equation for the linearized carbon dioxide change amount.

[0086]

[0087] Because linearized differential equations are ordinary differential equations, they require a solution for analysis. To solve linearized differential equations, we use the State Space Method proposed by Friedland Bernard.

[0088] As shown in Fig. 8, according to the State-Space Method, the state change vector over time is expressed as the state change vector and the input value change vector, and the change in the state value through the change in the input value is calculated through this.

[0089] If the equation presented in Fig. 8 is converted to the s-domain through Laplace transformation, the equation is solved, and then converted back to the t-domain, the equation for the state value of the current state can be found, and finally, the change equation can be expressed as a matrix and vector equation as in mathematical equation 8.

[0090] As a result, the complex differential equation can be expressed using only vector and matrix operations, and the equation is changed so that the amount of change in the control variable can be easily obtained through computer operations alone.

[0091]

[0092] The assumptions for the constants and variable values ​​used in the final expression of the variation equation presented in Equation 8 are as shown in Table 2.

[0093]

[0094] Figure 9 is a schematic diagram showing an example of a simulator GUI.

[0095] The simulator GUI includes a scenario modification area for entering numerical values ​​for the experiment, an execution mode decision area for deciding whether to continue the simulation under the same conditions, continue the simulation while changing factor values ​​(e.g., number of heat sources), or perform a simulation that adjusts the flow rate to reach the optimal temperature / humidity / carbon dioxide values ​​according to the convergence time, a convergence time specification area for specifying the convergence time when performing a simulation that adjusts the flow rate, an area for showing photos of the simulation results, an area for showing a summary of the simulation results, and an area for saving the execution results as a CSV file.

[0096] Figure 10 is a graph showing simulation results according to one embodiment of the present invention.

[0097] The control method of the flow control simulator for indoor air optimization according to the present invention is specifically described as follows.

[0098] Figure 11 is a flow chart showing a control method of a flow control simulator for indoor air optimization according to the present invention.

[0099] First, read the simulation initial value (S1101) and set the simulation initial value (S1102).

[0100] Then, define the values ​​used in the variation and governing equations (S1103) and start the simulation (S1104).

[0101] Next, the initial value of the governing equation is set (S1105), the change amount for each zone is calculated (S1106), and the calculated change amount is applied for each zone (S1107).

[0102] The process of calculating and applying state changes is repeated until the simulation time reaches the end time. (S1108)

[0103] The simulator's input values ​​are written in a text file and used, and values ​​that do not change during the simulation, such as the volume of the zone, are set only once at the beginning of the simulation.

[0104] And when the simulation starts by defining the values ​​used in the change amount and governing equation, the process of setting the initial value and calculating and applying the state change amount is repeated until the simulation time reaches the end time.

[0105] Figures 12a and 12b are graphs showing simulation results of a flow control simulator for indoor air optimization according to the present invention.

[0106] When comparing the temperature change rate simulated for the defined Work Zone with the temperature change rate for verification, it can be confirmed that the trend was accurately predicted, and the humidity and carbon dioxide are also the same.

[0107] The flow control simulator for optimizing indoor air quality according to the present invention described above and its control method define a differential equation of a control variable and estimate the amount of change in the control variable through computer calculation, thereby providing experimental data necessary for developing an optimal control algorithm for a VAV system in a short period of time. In addition, by estimating the amount of change in the control variable through only matrix and vector equation calculations using a computer, simulation for developing an optimal control algorithm is possible efficiently in terms of cost and time.

[0108] As described above, it will be understood that the present invention can be implemented in modified forms without departing from the essential characteristics of the present invention.

[0109] Therefore, the specified embodiments should be considered in an illustrative rather than a restrictive sense, and the scope of the present invention is indicated by the claims rather than the foregoing description, and all differences within the scope equivalent thereto should be construed as being included in the present invention.

[0110] The present invention relates to indoor air optimization, and more particularly, to a flow control simulator for indoor air optimization and a control method thereof, which enable obtaining experimental data usable for developing an optimal control algorithm for a VAV (Variable Air Volume) system in a short period of time.

Claims

1. An electronic device including at least one processor for simulating flow control for indoor air optimization, Experimental space modeling department that sets up control areas and assumptions for VAV system application; Governing equation selection and analysis section that selects the governing equation for each control variable; A governing equation linearization unit that performs discretization through linearization of the governing equations for operation; A governing equation solution processing unit that applies solutions to solve the governing equations; A simulator interface section that provides a simulator interface by applying a state-space model formula; A flow control simulator for indoor air optimization, characterized by including a result verification and validation unit for verifying and confirming results through simulation.

2. In paragraph 1, the experimental space modeling section, A flow control simulator for indoor air optimization, which assumes the Work Zone as a lumped system, the walls connected to the outside are assumed to have no heat capacity, so that heat exchange between the Work Zone and the outside occurs only through conduction, and the Zones are completely blocked, and the incoming and outgoing masses are assumed to be equal.

3. In paragraph 1, the variables to be controlled include temperature, humidity, and carbon dioxide, For each control variable, the change is calculated using the corresponding governing equation. A flow control simulator for indoor air optimization, characterized in that temperature variables are derived from the Energy Equation, and humidity and carbon dioxide are derived from the Mass Balance equation.

4. In paragraph 3, the temperature differential equation is such that the heat change rate inside the work zone is equal to the heat change rate by the supplied air, the heat load on the wall, and the heat load generated by people, Here, the variable ρ is density, V is volume, C is specific heat capacity, G is air flow rate, T is temperature, A is area, U is heat transfer coefficient, Q is heat amount, N is the number of heat sources, and the subscript a is air, a, n are values ​​escaping to the outside, a, s are values ​​entering the room, wall is wall, out is outside, and i is the wall number. A flow control simulator for indoor air optimization.

5. In paragraph 3, the humidity differential equation is such that the humidity change rate inside the work zone is equal to the sum of the humidity change rate due to the supplied air and the humidity change rate due to human breathing, Here, the variable ρ is density, V is volume, G is air flow rate, W is humidity, A is area, N is the number of heat sources, and the subscript a is air, a, n are values ​​escaping to the outside, a, s are values ​​entering the room, wall is wall, out is outside, i is wall number, and exhale is exhalation. A flow rate control simulator for indoor air optimization.

6. In paragraph 3, the carbon dioxide differential equation is such that the carbon dioxide change rate inside the Work Zone is equal to the carbon dioxide change rate by the supplied air and the carbon dioxide change rate by human breathing, Here, the variable ρ is density, V is volume, G is air flow rate, C is carbon dioxide, N is the number of heat sources, the subscript a is air, a, n are values ​​escaping to the outside, a, s are values ​​entering the room, wall is wall, out is outside, i is the wall number, and exhale is exhalation. A flow control simulator for indoor air optimization.

7. In paragraph 1, the governing equation linearization part, In order to reflect the change in the state value according to the input value and to enable computer calculation of the nonlinear system, a linearization process is performed to change the variable value into the form of the initial value and the amount of change. A flow control simulator for indoor air optimization characterized by using the State Space Method to solve linearized differential equations in the governing equation solution processing unit.

8. In paragraph 7, by applying the State-Space Method, The state change vector over time is expressed as a state change vector and an input value change vector, and the change in state value through the change in input value is calculated. A flow control simulator for indoor air optimization characterized by changing to the s-domain through Laplace transform, solving the equation, then converting back to the t-domain to check the equation of the state value of the current state, and finally expressing the change equation as a matrix and vector equation.

9. In paragraph 1, the simulator GUI, A scenario editing area for entering numerical values ​​for the experiment, An execution mode decision area that determines whether to continue the simulation under the same conditions or to continue the simulation while changing the factor values, or to perform a simulation that adjusts the amount of flow to reach the optimal temperature / humidity / carbon dioxide values ​​according to the convergence time. A convergence time designation region that specifies the convergence time when performing a simulation that controls the amount of flow, and The area that shows the simulation result photos, An area showing a summary of the simulation results, A flow control simulator for indoor air optimization, characterized by including an area for saving execution results as a CSV file.

10. A step of modeling the experimental space to set the control area and assumptions for applying the VAV system, and selecting the governing equation for each control variable; A step of performing discretization through linearization of the governing equations for operation and applying a solution to solve the governing equations; A step of setting the simulation initial value by reading the simulation initial value; A step of defining the values ​​used in the variation and governing equations and starting the simulation; A control method for a flow control simulator for indoor air optimization, characterized by including a step of setting an initial value of a governing equation, calculating a change amount by zone, and applying the calculated change amount by zone.

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